Utilities are facing uncertainty in future electricity demand, driven by new technology proliferation (EVs, heat pumps, solar panels, data centres, etc.) and a rapidly changing policy environment. This presentation draws on Fluvius, Belgium’s largest DSO, and lessons from its deployment of a data- and AI-driven modelling tool to identify no-regrets investments for regulatory filings, assess grid congestion and resolution strategies, and understand proposed policy impacts. 

Three “hows” for grid operators will be shared:

• How to Model Future Load Scenarios: Discover how Fluvius used localized models and Monte Carlo simulations to evaluate the grid impacts of electrification, considering consumer behaviour, policies, and macroeconomic factors.
• How to Translate Scenarios into Actionable Insights: Learn how Fluvius employed a digital twin of their grid to identify potential congestion and assess wired and non-wired solutions.
• How to Enhance Transparency and Collaboration in Grid Planning Processes: Explore how Fluvius and Belgian regulators used the future demand models and digital twin to align on scenario assumptions and collaboratively approve no-regret investments and rate case applications.

These insights provide utilities with strategies to reduce uncertainty, save modelling time, and support planners and regulators in decision making by integrating data- and AI-based solutions into grid planning processes.